مبانی محاسبات ماتریسی ۲۰۱۹
Fundamentals of Matrix Computations 2019

1. Cover

2. Title Page

3. Copyright

4. DECLARATION

5. ABOUT THE EDITOR

6. TABLE OF CONTENTS

7. List of Contributors

8. List of Abbreviations

9. Preface

10. Chapter 1 Singular Value Homogenization: a Simple Preconditioning Technique for Linearly Constrained Optimization and its Potential Applications in Medical Therapy

11. Chapter 2 Perturbation Bounds for Eigenvalues of Diagonalizable Matrices and Singular Values

12. Chapter 3 New Iterative Methods for Generalized Singular-value Problems

13. Chapter 4 Blind Distributed Estimation Algorithms for Adaptive Networks

14. Chapter 5 A DFT-based Approximate Eigenvalue and Singular Value Decomposition of Polynomial Matrices

15. Chapter 6 Canonical Polyadic Decomposition of Third-order Semi-nonnegative Semi-symmetric Tensors using LU and QR Matrix Factorizations

16. Chapter 7 Sparse Signal Subspace Decomposition based on Adaptive Over-complete Dictionary

17. Chapter 8 Lower Bounds for the Low-rank Matrix Approximation

18. Chapter 9 A Reduced-rank Approach for Implementing Higher-order Volterra Filters

19. Chapter 10 A Semi-smoothing Augmented Lagrange Multiplier Algorithm for Low-rank Toeplitz Matrix Completion

20. Chapter 11 Singular Spectrum-based MatrixCompletion for Time Series Recovery and Prediction

21. Chapter 12 An Effective Numerical Method to Solve a Class of Nonlinear Singular Boundary Value Problems using improved Differential Transform Method

22. Index

23. Back Cover